simuDM {scDD} | R Documentation

## simuDM

### Description

Simulation for Differential Modalities Case

### Usage

    simuDM(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC,
      coeff, RP, modeFC, generateZero, constantZero, varInflation)

### Arguments

`Dataset1` | Numeric matrix of expression values with genes in rows and samples in columns.
`Simulated_Data` | Required input empty matrix to provide structure information of output matrix with simulated data
`DEIndex` | Index for DE genes
`samplename` | The name for genes that chosen for simulation
`Zeropercent_Base` | Zero percentage for corresponding gene expression values
`f` | Fold change values (number of SDs) for each gene
`FC` | Fold Change values for DE Simulation
`coeff` | Relationship coefficients for Mean and Variance
`RP` | matrix for NB parameters for genes in samplename
`modeFC` | Vector of values to use for fold changes between modes for DP, DM, and DB.
`generateZero` | Specification of how to generate the zero values. If "`empirical`" (default), the observed proportion of zeroes in each gene is used for the simuated data, and the nonzeroes are simulated from a truncated negative binomial distribution. If "`simulated`", all values are simulated out of a negative binomial distribution, includling the zeroes. If "`constant`", then each gene has a fixed proportion of zeroes equal to `constantZero`.
`constantZero` | Numeric value between 0 and 1 that indicates the fixed proportion of zeroes for every gene. Ignored if `generateZero` method is not equal to "`constant`".
`varInflation` | Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor.

### Value

Simulated_Data Simulated dataset for DM

### References

Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y

[Package scDD version 1.8.0 Index]